Additional file 2 of Physiological and comparative transcriptome analyses reveal the mechanisms underlying waterlogging tolerance in a rapeseed anthocyanin-more mutant
Bibliographic record
Abstract
Additional file 2: Table S1. List of DEGs detected in the WT ZS11after the waterlogging treatment. Table S2. List of DEGs detected in the am mutant after the waterlogging treatment. Table S3. Differential expression of subclusters 1–5 in the am mutant. Table S4. Differential expression of subclusters 1–6 in the WT. Table S5. All the up- and downregulated DEGs with GO annotations in the am mutant. Table S6. All the up- and downregulated DEGs with GO annotations in the WT. Table S7. KEGG pathway enrichment of the DEGs in the WT. Table S8. KEGG pathway enrichment of the DEGs in the am mutant. Table S9. The DEG enrichment in the phenylalanine biosynthetic and metabolic pathways. Table S10. A list of differentially expressed TFs in the WT. Table S11. A list of differentially expressed TFs in the am mutant. Table S12. The DEG enrichment in plant hormone signal transduction pathways. Table S13. New transcript predictions as assessed by RNA-seq. Table S14. Primers used for qPCR validation of the selected DEGs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.755 | 0.121 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".